Integrated Assessment of Artisanal and Small-Scale Gold Mining in Ghana—Part 1: Human Health Review
Bibliographic record
Abstract
This report is one of three synthesis documents produced via an integrated assessment (IA) that aims to increase understanding of artisanal and small-scale gold mining (ASGM) in Ghana. Given the complexities surrounding ASGM, an IA framework was utilized to analyze economic, social, health, and environmental data, and co-develop evidence-based responses with pertinent stakeholders. The current analysis focuses on the health of ASGM miners and community members, and synthesizes extant data from the literature as well as co-authors' recent findings regarding the causes, status, trends, and consequences of ASGM in Ghana. The results provide evidence from across multiple Ghanaian ASGM sites that document relatively high exposures to mercury and other heavy metals, occupational injuries and noise exposure. The work also reviews limited data on psychosocial health, nutrition, cardiovascular and respiratory health, sexual health, and water and sanitation. Taken together, the findings provide a thorough overview of human health issues in Ghanaian ASGM communities. Though more research is needed to further elucidate the relationships between ASGM and health outcomes, the existing research on plausible health consequences of ASGM should guide policies and actions to better address the unique challenges of ASGM in Ghana and potentially elsewhere.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".